Computation–Communication Tradeoffs for Missing Multitagged Item Detection in RFID Networks
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Bibliographic record
Abstract
Missing item event detection is one of the most important radio-frequency identification (RFID)-enabled functions. Yet it is largely unaddressed how to fast and reliably detect missing item event in multitagged RFID systems where multiple tags are tagged on one item. The canonical methods can only solve tag-level detection problem where each item is associated with one tag, and applying them to detect the missing multitagged items would falsely alarm and is time inefficient. To bridge the gap, this article formulates and analyzes the missing multitagged item detection problem. Our key idea is to search the proper seeds so that the reader only needs to probe a subset of the tags each being selected from different items instead of the entire tag set for the missing item detection. By employing the computation-communication tradeoffs, we design two protocols named M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ID and M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ID+ that classifies the tags before the segmentation compared to the former to improve time efficiency. With the derived optimum parameters, our protocols can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times$ </tex-math></inline-formula> performance gain in terms of time efficiency compared with the state-of-the-art solution.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it